Immobilizing intersectionality: The performative inclusion of feminist expertise within PSI sexual violence policies
Bibliographic record
Abstract
In light of public scandals and legislative pressure, Canadian universities have instituted sexualized violence policies in an attempt to curb harm on campus. As the first step, policy-making committees and task forces were established to spearhead institutional change. Using data from 49 qualitative interviews with feminist faculty across Canada, we examine how these policy-making committees utilized feminist expertise, particularly whether feminists with intersectional positionalities and expertise were invited to the table and if their expertise was used to inform the resulting institutional policies. As our findings illustrate, even though policies profess to seek or incorporate intersectionality, experts in intersectionality– particularly those with intersectional positionalities– are rarely invited or heard. As we argue in this article, post-secondary institutions actively work against intersectionality by narrowing the mandates of committees and siloing task forces from other Equity, Diversity, and Inclusion (EDI) concerns. Additionally, invitations to serve as experts on sexualized violence committees are often reserved for feminists deemed by administrators to be palatable, and those invited who embody diversity are used to rubber stamp the process of creating sexualized violence responses instead of informing the policies. This article illustrates the various ways in which PSI committees' constitutions and their mandates tend to make intersectionality a performative rather than informative guiding principle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.052 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".